Match!

Estimating abundance with interruptions in data collection using open population spatial capture-recapture models

Published on Jun 14, 2019in bioRxiv
· DOI :10.1101/671461
Cyril Milleret5
Estimated H-index: 5
(NMBU: Norwegian University of Life Sciences),
Pierre Dupont3
Estimated H-index: 3
(NMBU: Norwegian University of Life Sciences)
+ 5 AuthorsRichard Bischof20
Estimated H-index: 20
(NMBU: Norwegian University of Life Sciences)
Abstract
The estimation of population size remains one of the primary goals and challenges in ecology and provides a basis for debate and policy in wildlife management. Despite the development of efficient non-invasive sampling methods and robust statistical tools to estimate abundance, maintenance of field sampling is still subject to economic and logistic constraints. These can result in intentional or unintentional interruptions in sampling and cause gaps in data time series, posing a challenge to abundance estimation, and ultimately conservation and management decisions. We applied an open population spatial capture-recapture (OPSCR) model to simulations and a real case study to test the reliability of abundance inferences models to interruption in data collection. Using individual detections occurring over consecutive sampling occasions, OPSCR models allow the estimation of abundance from individual detection data while accounting for lack of demographic and geographic closure between occasions. First, we simulated sampling data with interruptions in field sampling of different lengths and timing. We checked the performance of an OPSCR model in deriving abundance for species with slow and intermediate life history strategies. Finally, we introduced artificial sampling interruptions of various magnitudes and timing to a five-year non-invasive monitoring data set of wolverines (Gulo gulo) in Norway and quantified the consequences for OPSCR model predictions. Inferences from OPSCR models were reliable even with temporal interruptions in monitoring. Interruption did not cause any systematic bias, but increased uncertainty. Interruptions occurring at occasions towards the beginning and the end of the sampling caused higher uncertainty. The loss in precision was more severe for species with a faster life history strategy. We provide a reliable framework to estimate abundance even in the presence of sampling interruptions. OPSCR allows monitoring studies to provide contiguous abundance estimates to managers, stakeholders, and policy makers even when data are non-contiguous. OPSCR models do not only help cope with unintentional interruptions during sampling but also offer opportunities for using intentional sampling interruptions during the design of cost-effective population surveys.
  • References (43)
  • Citations (0)
📖 Papers frequently viewed together
16 Citations
16 Citations
78% of Scinapse members use related papers. After signing in, all features are FREE.
References43
Newest
#1Ben C. Augustine (Cornell University)H-Index: 4
#2Marc Kéry (Swiss Ornithological Institute)H-Index: 36
Last. Chris Sutherland (UMass: University of Massachusetts Amherst)H-Index: 12
view all 6 authors...
1 CitationsSource
espanolA traves de modelos de captura, marcaje y recaptura (CMR) se puede estimar la probabilidad de supervivencia en poblaciones naturales. Sin embargo, cuando las sesiones de captura estan espaciadas de manera desigual, los modelos dependientes de la edad pueden producir estimaciones erroneas de la supervivencia si los individuos cambian de clase de edad durante el intervalo entre dos sesiones de captura. Proponemos una solucion para corregir el desajuste entre los intervalos de tiempo y la du...
1 CitationsSource
#1Vincenzo Gervasi (University of Montpellier)H-Index: 1
#2John D. C. LinnellH-Index: 51
Last. Olivier Gimenez (CNRS: Centre national de la recherche scientifique)H-Index: 41
view all 4 authors...
2 CitationsSource
#1Cyril Milleret (NMBU: Norwegian University of Life Sciences)H-Index: 5
#2Pierre Dupont (NMBU: Norwegian University of Life Sciences)H-Index: 3
Last. Richard Bischof (NMBU: Norwegian University of Life Sciences)H-Index: 20
view all 7 authors...
2 CitationsSource
#1Beth Gardner (UW: University of Washington)H-Index: 26
#2Rahel Sollmann (UC Davis: University of California, Davis)H-Index: 6
Last. K. Ullas Karanth (NCBS: National Centre for Biological Sciences)H-Index: 30
view all 5 authors...
5 CitationsSource
#1Cyril Milleret (NMBU: Norwegian University of Life Sciences)H-Index: 5
#2Pierre Dupont (NMBU: Norwegian University of Life Sciences)H-Index: 3
Last. Richard Bischof (NMBU: Norwegian University of Life Sciences)H-Index: 20
view all 6 authors...
3 CitationsSource
#1José Vicente López-Bao (CSIC: Spanish National Research Council)H-Index: 29
#2Raquel Godinho (University of Porto)H-Index: 23
Last. José I. Jiménez (CSIC: Spanish National Research Council)H-Index: 16
view all 8 authors...
Decision-makers in wildlife policy require reliable population size estimates to justify interventions, to build acceptance and support in their decisions and, ultimately, to build trust in managing authorities. Traditional capture-recapture approaches present two main shortcomings, namely, the uncertainty in defining the effective sampling area, and the spatially-induced heterogeneity in encounter probabilities. These limitations are overcome using spatially explicit capture-recapture approache...
12 CitationsSource
#1Nicolas Lieury (AMU: Aix-Marseille University)H-Index: 3
#2Sébastien Devillard (University of Lyon)H-Index: 19
Last. Alexandre Millon (AMU: Aix-Marseille University)H-Index: 14
view all 7 authors...
Abstract Population monitoring traditionally relies on population counts, accounting or not for the issue of detectability. However, this approach does not permit to go into details on demographic processes. Therefore, Capture-Recapture (CR) surveys have become popular tools for scientists and practitioners willing to measure survival response to environmental change or conservation actions. However, CR surveys are expensive and their design is often driven by the available resources, without es...
3 CitationsSource
#1Perry de Valpine (University of California, Berkeley)H-Index: 21
#2Daniel Turek (University of California, Berkeley)H-Index: 6
Last. Rastislav Bodik (University of California, Berkeley)H-Index: 38
view all 6 authors...
ABSTRACTWe describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simula...
61 CitationsSource
#1Daniel Turek (University of California, Berkeley)H-Index: 6
#2Perry de Valpine (University of California, Berkeley)H-Index: 21
Last. Christopher J. Paciorek (University of California, Berkeley)H-Index: 40
view all 3 authors...
Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variables. When potentially many HMMs are embedded within a hierarchical model, this can result in prohibitively long MCMC runtimes. We study combinations of existing methods, which are shown to vastly improve computational efficiency for these hierarchical...
8 CitationsSource
Cited By0
Newest